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Tillé, Yves

Nom
Tillé, Yves
Affiliation principale
Site web
Fonction
Professeur ordinaire
Email
yves.tille@unine.ch
Identifiants
https://libra.unine.ch/handle/123456789/541
_
0000-0003-0904-5523

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  • Publication
    Métadonnées seulement
    Complex Sampling Design for a Long-Term Monitoring Program of the Agricultural Species and Habitats in Switzerland (ALL-EMA)
    (Zurich Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 2019)
    Ecker, Klaus
    ;
    Meier, Eliane
    ;
    Lanz, Adrian
    ;
    Tillé, Yves 
    We describe a complex probability sampling design for a long-term monitoring program of agricultural species and habitats in Switzerland. The program aims to monitor farmland biodiversity in predefined regions and to assess the effectiveness of funded management in promoting it. Such monitoring requires the costly collection of {\it in situ} information on species, habitat types and structures at the plot and landscape level. Sample efficiency is challenging since the majority of habitats and species is typically rare, spatially structured and previously unknown in the sampling frame. Efficient sampling aims to minimize the collection of redundant information from the big regions and the dominant habitat types. The sample should be spatially spread and balanced across environmental gradients. Decisions should be made to allocate the sampling effort within and across sample sites. Finally, the survey organization has to be simple to implement in the field. In Switzerland zoological data are already collected on a regular grid of 1 km$^2$. We propose an additional three-stage sampling scheme for the static survey of habitats and plant species on the total agrarian landscape. An extra sample scheme is defined to monitor areas with funded biodiversity management. Both sampling designs use modern sampling techniques, such as unequal probability sampling, balancing, spatial spreading and self-weighting to ensure sample efficiency at all sampling stages. The efficiency of balancing, spreading and sample size allocation is demonstrated in simulation studies. A power analysis suggests that changes of $5-10\%$ can be statistically detected for a majority of the target habitats.
  • Publication
    Métadonnées seulement
    Incorporating Spatial and Operational Constraints in the Sampling Designs for Forest Inventories
    (2015-9-3)
    Vallée, Audrey-Anne 
    ;
    Ferland-Raymond, Bastien
    ;
    Rivest, Louis-Paul
    ;
    Tillé, Yves 
    Goals of forest inventories include understanding the forest temporal evolution and monitoring fragile ecosystems. In the province of Quebec, Canada, their implementation faces challenging methodological problems. The survey area covers a large territory which is hardly accessible and has diverse forest. Main operational goals are to spread the sampled plots throughout the survey area and to well represent all forest types in the sample. They are hard to achieve while keeping the costs within budget. Usually, a two dimensional systematic sampling design is applied and the rich auxiliary information is only used at the estimation stage. We show how to use modern and advanced sampling techniques to improve the planning of forest inventories, considering many requirements. For the Quebec forest inventory, we build a two-stage sampling design that has clusters of plots to optimize field work and predetermined sample sizes for forest types. Constraints of spreading the sample in the whole territory and of balancing according to auxiliary variables are also implemented. To meet these requirements, we use unequal inclusion probabilities, balanced sampling, highly stratified balanced sampling, and sample spreading. The impact of these novel techniques on the implementation of requirements and on the precision of survey estimates is investigated using Quebec inventory data.
  • Publication
    Métadonnées seulement
    Incorporating spatial and operational constraints in the sampling designs for forest inventories
    (2015-7-15)
    Vallée, Audrey-Anne 
    ;
    Ferland-Raymond, Bastien
    ;
    Rivest, Louis-Paul
    ;
    Tillé, Yves 
    In the province of Quebec, Canada, the forest is examined through regular inventories. Requirements for the spreading and the type of trees and for the cost are difficult to manage. We show that modern and advanced sampling techniques can be used to improve the planning of the forest inventories, even if there are many requirements. Our design includes balanced sampling, highly stratified balanced sampling and sample spreading through a two stage sample. The impact of these techniques on the satisfaction of the requirements and on the precision of survey estimates is investigated using field data from a Quebec inventory.
  • Publication
    Métadonnées seulement
    Incorporating spatial and operational constraints in the sampling designs for forest inventories
    (2015-6-15)
    Vallée, Audrey-Anne 
    ;
    Ferland-Raymond, Bastien
    ;
    Rivest, Louis-Paul
    ;
    Tillé, Yves 
    In the province of Quebec, Canada, the forest is examined through regular inventories. Requirements for the spreading and the type of trees and for the cost are difficult to manage. We show that modern and advanced sampling techniques can be used to improve the planning of the forest inventories, even if there are many requirements. Our design includes balanced sampling, highly stratified balanced sampling and sample spreading through a two stage sample. The impact of these techniques on the satisfaction of the requirements and on the precision of survey estimates is investigated using field data from a Quebec inventory.
  • Publication
    Métadonnées seulement
    Bias Robustness and Efficiency in Model-Based Inference
    (2012-9-4)
    Nedyalkova, Desislava 
    ;
    Tillé, Yves 
    In model-based inference, the selection of balanced samples has been considered to give protection against misspecification of the model. A recent development in finite population sampling is that balanced samples can be randomly selected. There are several possible strategies that use balanced samples. We give a definition of balanced sample that embodies overbalanced, mean-balanced, and $\pi$-balanced samples, and we derive strategies in order to equalize a $d$-weighted estimator with the best linear unbiased estimator. We show the value of selecting a balanced sample with inclusion probabilities proportional to the standard deviations of the errors with the Horvitz-Thompson estimator. This is a strategy that is design-robust and efficient. We show its superiority compared to other strategies that use balanced samples in the model-based framework. In particular, we show that this strategy is preferable to the use of overbalanced samples in the polynomial model. The problem of bias-robustness is also discussed, and we show how overspecifying the model can protect against misspecification.